The Stock Market Challenge, Winton's second recruiting competition on Kaggle, asked participants to predict intra and end of day stock returns. The competition was crafted by research scientist at Winton to mimic the type of problem that they work on everyday. Mendrika Ramarlina finished third in the competition with a combination of simple models and intelligently engineered features.

The Basics

What was your background prior to entering this challenge?

I come from a software engineering background. I have 4 years of experience building data-centric web applications. I have been taking online machine learning classes, reading research papers, and implementing models since 2012.

How did you get started competing on Kaggle?

I found out about Kaggle from an article on Bloomberg from back in 2012. At the time, I had just completed Andrew Ng's class. I wanted to start working on projects involving machine learning, but wasn't sure what to work on. Kaggle competitions were a great way to get to started.

What made you decide to enter this competition?

Forecasting stock returns is a very interesting problem. I have been applying machine learning to the financial market for a few years now. I joined to see how some of my approaches would fare in comparison to others'.

Let's Get Technical

What preprocessing and supervised learning methods did you use?

Feature engineering was a key element in my approach. My final solution was an ensemble made up of 2 SVMs and 1 regularized linear regression all trained on handcrafted features such as peak-to-valley drawdown magnitude and duration or cumulated intraday returns.

Which tools did you use?

For preprocessing and model training, I used a standard pythonic machine learning stack: Pandas, Numpy, Scikit-Learn. I used matplotlib for visualization, and I did use some Excel for data exploration.

How did you spend your time on this competition?

I spent the majority of my time analyzing the data, engineering features. Instead of training and fine- tuning complex models, my approach was to use simple models, then create features that would help improve the performance of these models.

Words of Wisdom

What have you taken away from this competition?

That it is very difficult to predict minute-wise intraday data using historical data alone.

Do you have any advice for those just getting started in data science?

Don't get hung up on one technology stack, tool, or algorithm

Understand how different statistical models “learn” from the data

Keep up with research

Just for Fun

If you could run a Kaggle competition, what problem would you want to pose to other Kagglers?

I would start a competition to predict what crime will occur when and where, given historical crime data.

Bio

Mendrika Ramarlina is a founder and machine learning engineer at Madagascar Innovation Lab, a startup that uses machine learning to solve problems with social impacts in Madagascar. His interests include deep learning and reinforcement learning.